---
title: Genomic Feature Analysis of CMR
output:
flexdashboard::flex_dashboard:
orientation: rows
social: menu
source_code: embed
theme: cosmo
---
```{r setup, include=FALSE}
library(flexdashboard)
library(rtracklayer)
library(plotly)
library(ggplot2)
library(seqinr)
library(GenomicFeatures)
library(knitr)
library(kableExtra)
options(scipen = 20)
knitr::opts_chunk$set(echo = TRUE,
warning = FALSE,
message = FALSE)
```
```{r echo = FALSE, warning = FALSE, message = FALSE}
CMRGene <- read.table(file = CMRGeneDir, sep = "\t", header = F, quote = '',
stringsAsFactors = F)
CMRGene <- CMRGene$V1
GTF <- import(con = GTFDir)
GTF <- subset(GTF, GTF$gene_biotype == "protein_coding")
txdb <- makeTxDbFromGFF(file = GTFDir)
seqNames <- na.omit(as.numeric(as.character(unique(seqnames(GTF)@values))))
GTFdf <- data.frame(GTF)
resFreq <- NULL
resExonNumber <- NULL
for(i in 1:length(seqNames)){
curGTF <- subset(GTFdf, GTFdf$seqnames == i)
exonNumber <- by(data = curGTF, INDICES = curGTF[, "gene_id"],
function(x) length(na.omit(unique(x[,"exon_number"]))))
curGeneGTF <- subset(curGTF, curGTF$type == "gene")
curGeneGTF <- curGeneGTF[order(curGeneGTF$start), ]
rownames(curGeneGTF) <- curGeneGTF$gene_id
orderGene <- rownames(curGeneGTF)
exonNumber <- exonNumber[orderGene]
curLength <- length(exonNumber)
curStart <- seq(1, curLength - seqLength + stepSize, stepSize)
curEnd <- c(curStart[1:(length(curStart)-1)] + seqLength - 1, curLength)
curMat <- cbind(curStart, curEnd)
meanExonNumber <- apply(curMat, 1,
function(x) mean(exonNumber[x[1]:x[2]]))
CMRFreq <- apply(curMat, 1,
function(x) length(intersect(names(exonNumber)[x[1]:x[2]],
CMRGene))/seqLength)
resFreq <- c(resFreq, CMRFreq)
resExonNumber <- c(resExonNumber, meanExonNumber)
}
```
### Introduction
- The function provides the correlation analysis of CMR genes with multiple gene features including **Mean gene length**, **Mean exon number**, **Mean GC content** and **Mean distance to adjacent gene**. To be specific, we split the maize genome in a sliding window of 100 adjacent genes with step size of 10, and calculate the frequency of CMR genes in each window (100 adjacent genes).
Row {data-height=250}
---------------------------------------------------------------
### Correlation of CMR genes with mean exon number
```{r echo = FALSE, warning=FALSE, message=FALSE}
df <- data.frame(freq = resFreq*100,
exonNumber = resExonNumber)
p1 <- ggplot(df, aes(x = freq, y = exonNumber)) +
geom_point(colour = "grey") +
geom_smooth(method = "lm", se = FALSE, colour = "red") +
labs(x = "Frequency of CMR genes (%)", y = "Mean exon number")
ggplotly(p1)
```
### Statistics of linear regression of CMR genes with mean exon number
```{r echo = FALSE, warning = FALSE, message = FALSE}
group <- gl(n = 2, k = nrow(df), labels = c("freq", "intronLength"))
values <- c(df$freq, df$exonNumber)
lmRes <- lm(values ~ group)
res.summary <- summary(lmRes)
res.table <- data.frame(Stats = c("r.squared",
"adj.r.squared",
"fstatistic",
"sigma",
"df"),
values = c(res.summary$r.squared,
res.summary$adj.r.squared,
paste(res.summary$fstatistic, collapse = " "),
res.summary$sigma,
paste(res.summary$df, collapse = " ")))
knitr::kable(x = res.table, align = 'c') %>%
kableExtra::kable_styling(position = 'center', full_width = FALSE,
stripe_color = 'black', latex_options = "bordered")
```
### Anova analysis of CMR genes with mean exon number
```{r echo = FALSE, warning = FALSE, message = FALSE}
knitr::kable(x = t(anova(lmRes)), align = "c") %>%
kableExtra::kable_styling(position = "center", full_width = FALSE,
stripe_color = "black", latex_options = "bordered")
```
Row {data-height=250}
---------------------------------------------------------------
```{r echo = FALSE, warning = FALSE, message = FALSE}
nonCMRGene <- setdiff(unique(GTFdf$gene_id), CMRGene)
nonCMRGene <- sample(nonCMRGene, size = ratio*length(CMRGene))
```
### Exon number difference between CMR genes and non-CMR genes
```{r echo = FALSE, warning=FALSE, message=FALSE}
cmr.gtf <- GTFdf[which(!is.na(match(GTFdf$gene_id, CMRGene))), ]
cmr.exonNumber <- as.numeric(unlist(by(data = cmr.gtf, INDICES = cmr.gtf[, "gene_id"],
function(x) length(na.omit(unique(x[,"exon_number"]))))))
noncmr.gtf <- GTFdf[which(!is.na(match(GTFdf$gene_id, nonCMRGene))), ]
noncmr.exonNumber <- as.numeric(unlist(by(data = noncmr.gtf, INDICES = noncmr.gtf[, "gene_id"],
function(x) length(na.omit(unique(x[,"exon_number"]))))))
cmrStat <- boxplot.stats(cmr.exonNumber)$stats
tmpCMR <- cmr.exonNumber[which(cmr.exonNumber <= cmrStat[5])]
noncmrStat <- boxplot.stats(noncmr.exonNumber)$stats
tmpNonCMR <- noncmr.exonNumber[which(noncmr.exonNumber <= noncmrStat[5])]
df <- data.frame(type = factor(rep(c("CMRGene", "nonCMRGene"),
c(length(tmpCMR), length(tmpNonCMR)))),
value = c(tmpCMR, tmpNonCMR))
p <- ggplot(df, aes(x=type, y=value, fill=type)) +
geom_violin(trim = FALSE) +
theme(axis.title.x = element_blank(),
#axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
legend.title = element_blank(),
legend.position = "none")
ggplotly(p)
```
### The statistics of exon number difference between CMR genes and non-CMR genes
```{r echo = FALSE, warning=FALSE, message=FALSE}
res <- data.frame(Stats = c("Min", "First quantile", "Median", "Third quantile", "Max"),
CMR = cmrStat, nonCMR = noncmrStat)
knitr::kable(x = res, align = 'c') %>%
kableExtra::kable_styling(position = 'center', full_width = FALSE,
stripe_color = 'black', latex_options = "bordered")
```
### Exon number density difference between CMR genes and non-CMR genes
```{r echo = FALSE, warning = FALSE, message = FALSE}
p <- ggplot(df, aes(x=value, fill=type)) +
geom_density(alpha = 0.5) +
theme(axis.title.x = element_blank(),
#axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
legend.title = element_blank(),
legend.position = "none")
ggplotly(p)
```